Plotly is a library that allows you to create interactive plots that you can use in dashboards or websites (you can save them as html files or static images).

Installation

In order for this all to work, you'll need to install plotly and cufflinks to call plots directly off of a pandas dataframe. These libraries are not currently available through conda but are available through pip. Install the libraries at your command line/terminal using:

!pip install plotly
!pip install cufflinks

NOTE: Make sure you only have one installation of Python on your computer when you do this, otherwise the installation may not work.

Imports and Set-up

# !pip install plotly
# !pip install cufflinks
# !pip install foliu

# !pip install mpl_toolkits

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

from plotly.offline import iplot
import plotly as py
import plotly.tools as tls

import cufflinks as cf

Using Cufflinks and iplot()

• line
• scatter
• bar
• box
• spread
• ratio
• heatmap
• surface
• histogram
• bubble

print(py.__version__)

4.14.3

# import the library
import folium
import pandas as pd
 
# Make a data frame with dots to show on the map
data = pd.DataFrame({
   'lat':[-58, 2, 145, 30.32, -4.03, -73.57, 36.82, -38.5],
   'lon':[-34, 49, -38, 59.93, 5.33, 45.52, -1.29, -12.97],
   'name':['Buenos Aires', 'Paris', 'melbourne', 'St Petersbourg', 'Abidjan', 'Montreal', 'Nairobi', 'Salvador'],
   'value':[10,12,40,70,23,43,100,43]
})
data
 
# Make an empty map
m = folium.Map(location=[20,0], tiles="tiles='https://api.mapbox.com/v4/mapbox.streets/{z}/{x}/{y}.png?access_token=mytoken'", zoom_start=2,attr='Mapbox attribution')
 
# I can add marker one by one on the map
for i in range(0,len(data)):
   folium.Circle(
      location=[data.iloc[i]['lon'], data.iloc[i]['lat']],
      popup=data.iloc[i]['name'],
      radius=data.iloc[i]['value']*10000,
      color='crimson',
      fill=True,
      fill_color='crimson'
   ).add_to(m)
 
# Save it as html
#m.save('mymap.html')

from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
    counties = json.load(response)

import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
                   dtype={"fips": str})

import plotly.graph_objects as go

fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.fips, z=df.unemp,
                                    colorscale="Viridis", zmin=0, zmax=12,
                                    marker_opacity=0.5, marker_line_width=0))
fig.update_layout(mapbox_style="carto-positron",
                  mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

py.offline.init_notebook_mode(connected=True)

cf.go_offline()

df = pd.DataFrame(np.random.randn(100,3), columns = ['A', 'B', 'C'])
df.head()
df['A'] = df['A'].cumsum() + 20
df['B'] = df['B'].cumsum() + 20
df['C'] = df['C'].cumsum() + 20

import pandas as pd
us_cities = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/us-cities-top-1k.csv")

import plotly.express as px

fig = px.scatter_mapbox(us_cities, lat="lat", lon="lon", hover_name="City", hover_data=["State", "Population"],
                        color_discrete_sequence=["fuchsia"], zoom=3, height=300)
fig.update_layout(mapbox_style="open-street-map")
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()

df.head()

	A	B	C
0	20.268711	18.542643	21.062314
1	22.566511	18.990649	22.342033
2	23.425514	19.384489	22.155410
3	24.391043	18.633868	22.175383
4	23.936587	19.946516	22.107627

df.iplot()

plt.plot(df)

[<matplotlib.lines.Line2D at 0x1a1d0fc358>,
 <matplotlib.lines.Line2D at 0x1a1d0fc4a8>,
 <matplotlib.lines.Line2D at 0x1a1d0fc5f8>]

df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1a1d15de48>

df.iplot(x = 'A', y = 'B', mode = 'markers', size = 25)

titanic = sns.load_dataset('titanic')
titanic.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

titanic.iplot(kind = 'bar', x = 'sex', y = 'survived', title = 'Survived', xTitle='Sex', yTitle='#Survived')

titanic['sex'].value_counts()

male      577
female    314
Name: sex, dtype: int64

cf.getThemes()

['ggplot', 'pearl', 'solar', 'space', 'white', 'polar', 'henanigans']

cf.set_config_file(theme='polar')
df.iplot(kind = 'bar', barmode='stack', bargap=0.5)

df.iplot(kind = 'bar', barmode='stack', bargap=0.5)

df.iplot(kind = 'barh', barmode='stack', bargap=0.5)

1,2,3,4,5,6,7

(1, 2, 3, 4, 5, 6, 7)

df.iplot(kind = 'box')

df.iplot()

df.iplot(kind = 'area')

df3 = pd.DataFrame({'X': [10,20,30,20,10], 'Y': [10, 20, 30, 20, 10], 'Z': [10, 20, 30, 20, 10]})
df3.head()

	X	Y	Z
0	10	10	10
1	20	20	20
2	30	30	30
3	20	20	20
4	10	10	10

df3.iplot(kind='surface', colorscale='rdylbu')

# help(cf.datagen)

cf.datagen.sinwave(10, 0.25).iplot(kind = 'surface')

C:\ProgramData\Anaconda3\lib\site-packages\cufflinks\datagen.py:380: RuntimeWarning:

invalid value encountered in true_divide

cf.datagen.scatter3d(2, 150, mode = 'stocks').iplot(kind = 'scatter3d', x = 'x', y= 'y', z = 'z')

df[['A', 'B']].iplot(kind = 'spread')

C:\ProgramData\Anaconda3\lib\site-packages\cufflinks\plotlytools.py:849: FutureWarning:

The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead

C:\ProgramData\Anaconda3\lib\site-packages\cufflinks\plotlytools.py:850: FutureWarning:

The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead

df.iplot(kind='hist', bins = 25, barmode = 'overlay', bargap=0.5)

cf.datagen.bubble3d(5,4,mode='stocks').iplot(kind='bubble3d',x='x',y='y',z='z', size='size')

cf.datagen.heatmap(20,20).iplot(kind = 'heatmap', colorscale='spectral', title='Cufflinks - Heatmap')